Ithaca College

What you'll study

The programme is built around a core sequence in calculus and linear algebra and progresses to advanced courses in real analysis and mathematical modelling. Emphasis is placed on computational methods and the use of modern software tools to implement and test mathematical solutions.

• Core mathematics: multivariable calculus, linear algebra, ordinary differential equations, real analysis.
• Computational and applied courses: numerical analysis, scientific computing, numerical linear algebra, numerical solution of PDEs, mathematical modelling.
• Probability and statistics: probability theory, mathematical statistics, statistical methods for data analysis.
• Computer science and tools: programming for mathematicians (commonly Python, MATLAB or similar), algorithms, data structures, and version control/workflow practices for reproducible computation.
• Advanced topics and electives: optimisation, discrete mathematics, complex analysis, dynamical systems, computational geometry, machine learning electives where available.
• Capstone or independent research: a senior project, practicum or honours thesis applying computational techniques to a substantive problem; opportunities exist for faculty‑mentored research and interdisciplinary projects with science and engineering departments.

Entry requirements

Admission to the undergraduate programme is based on academic preparation and overall application strength. Typical expectations include a strong secondary school record with emphasis on mathematics.

• Academic preparation: completion of high‑school mathematics including algebra, geometry and preferably calculus (or equivalent college preparatory coursework).
• Recommended subjects: calculus, physics and computer science or programming experience are advantageous.
• Application materials: official school transcripts, personal statement, and letters of recommendation. Applicants may also demonstrate readiness via advanced placement or international qualifications where applicable.
• Selection considerations: the department looks for quantitative aptitude, problem‑solving ability and motivation for computational work; prior programming experience strengthens an application but is not always required.

Career prospects

Graduates leave with a strong foundation in both theory and computation, making them attractive to employers across multiple sectors. Career paths typically include technical and analytical roles that rely on quantitative reasoning and coding skills.

• Data analyst, data scientist and business analytics roles in industry and government.
• Software engineer or developer positions that value mathematical modelling and algorithmic thinking.
• Quantitative roles in finance, risk analysis and actuarial work.
• Research and technical roles in engineering, environmental modelling, and scientific computing.
• Teaching and further academic study: graduates often pursue graduate degrees (Masters or PhD) in mathematics, applied mathematics, computer science, statistics or related fields.

Why study at Ithaca College

Ithaca College offers a personalised undergraduate experience with small class sizes and close access to faculty, which benefits students tackling rigorous mathematical material and research projects. The college’s liberal arts environment encourages interdisciplinary learning, allowing computational mathematics students to collaborate with programmes in computer science, physics and business.

• Faculty mentorship: opportunities for supervised research and independent study with faculty who work across applied and computational areas.
• Hands‑on learning: access to computing facilities and laboratory resources for numerical experiments, coding projects and data analysis.
• Internships and local connections: proximity to a regional technology and research community, and campus career services that help place students in internships and employment.
• Preparation for next steps: a curriculum designed to support both entry into technical careers and preparation for graduate study, with emphasis on communication, reproducible computing and collaborative problem solving.